Embodiments of the invention are directed, in general, to communication systems and, more specifically, methods of detecting lack of movement to aid GNSS receivers.
As Global Navigation Satellite System (GNSS) receivers become more common, users continue to expect improved performance in increasingly difficult scenarios. GNSS receivers may process signals from one or more satellites from one or more different satellite systems. Currently existing satellite systems include global positioning system (GPS), and the global navigation satellite system (GLONASS). Systems expected to become operational in the near future include Galileo, quazi-zenith satellite system (QZSS), and Beidou. For many years, inertial navigation (IN) systems have been used in high-cost applications such as airplanes to aid GNSS receivers in difficult environments. One example that uses inertial sensors to allow improved carrier-phase tracking may be found in A. Soloviev, S. Gunawardena, and F. van Graas, “Deeply integrated GPS/Low-cost IMU for low CNR signal processing: concept description and in-flight demonstration,” Journal of the Institute of Navigation, vol. 55, No. 1, Spring 2008; incorporated herein by reference. The recent trend is to try to integrate a GNSS receiver with low-cost inertial sensors to improve performance when many or all satellite signals are severely attenuated or otherwise unavailable. The high-cost and low-cost applications for these inertial sensors are very different because of the quality and kinds of sensors that are available. The problem is to find ways that inexpensive or low-cost sensors can provide useful information to the GNSS receiver.
Low-cost sensors may not be able to provide full navigation data. Or they may only work in some scenarios. For example, an inertial sensor may be accurate while it is in a car, but inaccurate when carried by a pedestrian. In the past, most integration techniques for GNSS receivers and sensors assumed the sensors constituted a complete stand-alone navigation system or that its expensive components allow it to give precise measurements. Low-cost sensors cannot always allow for these assumptions. In addition, traditionally the IN system is assumed to be fully calibrated, which is not always possible.
One of the challenging use-cases for a GNSS receiver is when it is in an environment with poor satellite visibility (such as indoors) S. Schon, O. Bielenberg, “On the capability of high sensitivity GPS for precise indoor positioning,” 5th Workshop on Positioning, Navigation and Communication, pp. 121-127, March 2008; incorporated herein by reference. The typical approach is to integrate the received signal over a longer duration to boost the SNR enough to be able to synchronize to the satellites F. van Diggelen, “Indoor GPS theory & implementation,” IEEE Position Location and Navigation Symposium, pp 240-247, April 2002; incorporated herein by reference. This approach is feasible, but is very complex when the satellite signal levels are low. The proposed solution is better than this idea in several ways that are discussed below. Other existing approaches use other types of signals for determining location indoors, or use both GNSS signals and other signals in difficult scenarios J. Gonzalez, J. L. Blanco, et. al, “Combination of UWB and GPS for indoor-outdoor vehicle localization,” IEEE International Symposium on Intelligent Signal Processing, pp. 1-6, October 2007; incorporated herein by reference. This proposal focuses on improving the performance of GNSS signals since they are the most reliable signals, and they are universally available already. However, the GNSS signals when processed as we propose here can also be used along with the processing of other signals.
GNSS receivers rely on correlating a known pseudo random sequence (also called a PN sequence) with the received signal to synchronize to the transmitted signal. For different GNSS systems the PN sequences are different, different patterns, different lengths, etc. Typically, the results of correlating the PN sequence to different segments of the received signal are combined to increase the SNR. A coherent combination is when the correlation results are added after compensating for phase rotation. One kind of non-coherent combination adds the magnitude of the correlation results. Due to phase differences between the correlation results, the coherent combination provides more SNR gain if the phase is known with sufficient accuracy. In many cases a data bit is modulated onto the PN sequence at the transmitter. This data bit can be seen as a change in phase, so it doesn't affect the non-coherent combination, but if the phase changes due to the data-bits aren't accounted for they can severely degrade the coherent combination.
The global positioning system (GPS) is a system using GPS satellites for broadcasting GPS signals having information for determining location and time. Each GPS satellite broadcasts a GPS signal having message data that is unique to that satellite. The message for a Coarse/Acquisition (C/A) format of the GPS signal has data bits having twenty millisecond time periods. The twenty millisecond data bits are modulated by a one millisecond pseudorandom noise (PRN) code having 1023 bits or chips. The PRN code for each GPS satellite is distinct, thereby enabling a GPS receiver to distinguish the GPS signal from one GPS satellite from the GPS signal from another GPS satellite. The twenty millisecond GPS data bits are organized into thirty second frames, each frame having fifteen hundred bits. Each frame is subdivided into five subframes of six seconds, each subframe having three hundred bits.
One of the important figures of merit for a GPS receiver is its time to first fix, or the time period that it takes the GPS receiver from the time that it is turned on to the time that it begins providing its position and/or time to a user. In order to make this time period short, GPS receivers may be designed for what is sometimes known as a hot start. For a hot start, the GPS receiver starts acquisition with information for its own approximate location, an approximate clock time, and ephemeris parameters for the locations-in-space of the GPS satellites.
For a hot start, when the GPS receiver is turned on or returns to active operation from a standby mode, the GPS receiver processes its approximate time and location with the almanac or ephemeris information to determine which of the GPS satellites should be in-view and generates GPS replica signals having carrier frequencies and pseudorandom noise (PRN) codes matching the estimated Doppler-shifted frequencies and the PRN codes of the in-view GPS satellites. A search pattern or fast Fourier transform is used to find correlation levels between the replica signals and the carrier frequency and the PRN code of the incoming GPS signal. A high correlation level shows that GPS signal acquisition has been achieved at the frequency, code and code phase of the replica and the GPS receiver may begin tracking the frequency and the time-of-arrival of the code of the incoming GPS signals. At this point the GPS receiver knows the timing of the GPS data bits but it cannot determine its position because it does not yet know the absolute GPS clock time.
The GPS clock time is conventionally determined by monitoring the GPS data bits until a TLM is recognized for the start of a subframe. Following the TLM word, the GPS receiver reads a Zcount in the GPS data bits in a hand over word (HOW) to learn a GPS clock time. A current precise location-in-space of the GPS satellite is calculated from the GPS clock time and the ephemeris information. The time-of-arrival of the code of the GPS replica signal is then used to calculate a pseudorange between the location of the GPS receiver and the location-in-space of the GPS satellite. The geographical location fix is derived by linearizing the pseudorange for the approximate location of the GPS receiver and then solving four or more simultaneous equations having the linearized pseudoranges for four or more GPS satellites.
What is needed is Improved GNSS performance in harsh environments such as indoors, parking garages, deep urban canyons, and the like.